Single Image Processing¶
In [1]:
#from tools import init
import os
import tools
import glob
import cv2
import numpy as np
import pickle
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import scipy
from scipy import signal
from collections import deque
out_dir = 'output_images/step2/'
tools.init()
Undistort test images with visualization¶
In [2]:
# Undistort test images with visualization
%matplotlib inline
images = glob.glob('test_images/*.jpg')
gs = gridspec.GridSpec(8, 2)
gs.update(wspace=0.01, hspace=0.02) # set the spacing between axes.
plt.figure(figsize=(8,2))
for idx, fname in enumerate(images):
img = cv2.imread(fname)
dst = tools.undistort_img(img)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(cv2.cvtColor(dst, cv2.COLOR_BGR2RGB))
ax2.set_title('Undistorted Image', fontsize=30)
image_name=os.path.split(fname)[1]
write_name = out_dir + 'undistorted_' + image_name
cv2.imwrite(write_name,dst)
#cv2.imshow('undistorted', dst)
#cv2.waitKey(500)
cv2.destroyAllWindows()
Image binarization¶
In [3]:
def gaussian_blur(img, kernel_size=3):
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
def binarize(img,
s_thresh=(90, 255),
l_thresh=(40, 255),
sx_thresh=(20, 100), ksize_sx=3#11
):
# Convert to HLS color space and separate the L & S channels
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
l_channel = hls[:,:,1]
s_channel = hls[:,:,2]
# Sobel x
sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0, ksize=ksize_sx) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
# Threshold lightness
l_binary = np.zeros_like(l_channel)
l_binary[(l_channel >= l_thresh[0]) & (l_channel <= l_thresh[1])] = 1
binary = np.zeros_like(l_binary)
binary[(l_binary == 1) & (s_binary == 1) | (sxbinary == 1)] = 1
kernel = np.ones((3, 3), binary.dtype)
# remove white blobs
#binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
# fill black holes
#binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
# Stack each channel
# Note color_binary[:, :, 0] is all 0s, effectively an all black image. It might
# be beneficial to replace this channel with something else.
# color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary))
color_binary = np.dstack((l_binary, sxbinary, s_binary))
binary = (np.dstack(( binary, binary, binary))*255.).astype('uint8')
return binary, color_binary
def binarize_img(img):
binary,_ = binarize(img)
return binary
In [4]:
image = mpimg.imread('test_images/test5.jpg')
image = tools.undistort_img(image)
_,color_binary = binarize(image)
assert(color_binary is not None)
plt.imsave(out_dir + 'binary_test5.jpg', color_binary)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(color_binary)
ax2.set_title('Binarized Image', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [5]:
images = glob.glob('test_images/*.jpg')
gs = gridspec.GridSpec(8, 2)
gs.update(wspace=0.01, hspace=0.02) # set the spacing between axes.
plt.figure(figsize=(8,2))
for idx, fname in enumerate(images):
image = mpimg.imread(fname)
image = tools.undistort_img(image)
# image = gaussian_blur(image)
binary = binarize_img(image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(binary)
ax2.set_title('Binarized Image', fontsize=30)
image_name=os.path.split(fname)[1]
write_name = out_dir + 'binary_' + image_name
plt.imsave(write_name, binary)
#print(write_name)
cv2.destroyAllWindows()
Perspective Transform¶
In [6]:
bird_corners = tools.birdview_corners()
image = mpimg.imread('test_images/straight_lines1.jpg')
image = tools.undistort_img(image)
corner_tuples = []
for i,_ in enumerate(bird_corners):
corner_tuples.append(tuple(bird_corners[i]))
# draw: bottom-left, top-left, top-right, bottom-right
for i, j in [(0,1), (1,2), (2,3), (3,0)]:
cv2.line(image, corner_tuples[i], corner_tuples[j], color=[255,0,0], thickness=1)
warped = tools.warp_img(image)
plt.imsave(out_dir + 'straight_lines.jpg', image)
plt.imsave(out_dir + 'warped_straight_lines.jpg', warped)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(25, 10))
f.tight_layout()
ax1.set_title('Undistorted Image with source points drawn', fontsize=35)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.imshow(image)
ax2.set_title('Warped result with dest. points drawn', fontsize=35)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.imshow(warped)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
ROI to mask noise at the left and right sides of frame¶
In [7]:
image = mpimg.imread('test_images/test5.jpg')
image = tools.undistort_img(image)
warp = tools.warp_img(image)
warp_roi = tools.ROI(warp)
binary = binarize_img(image)
binary = tools.warp_img(binary)
binary_roi = tools.ROI(binary)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(25, 10))
f.tight_layout()
ax1.set_title('Undistorted Image', fontsize=35)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.imshow(image)
ax2.set_title('Warped result with ROI', fontsize=35)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.imshow(warp_roi)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(25, 10))
f.tight_layout()
ax1.set_title('Warped Binary result', fontsize=35)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.imshow(binary)
ax2.set_title('Warped binary result with RIO', fontsize=35)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.imshow(binary_roi)
Out[7]:
Finding Lanes¶
In [8]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
from tools import binarize_pipeline
from detect_lane import find_peaks
In [9]:
# Test find_peaks
from detect_lane import find_peaks
img = mpimg.imread('test_images/test5.jpg')
binary = binarize_pipeline(img)
left_peak = find_peaks(binary, 300, verbose=True)
right_peak = find_peaks(binary, 1000, verbose=True)
In [10]:
# Detect left and right lines
from detect_lane import detect_line, draw_lanes_with_windows
%matplotlib inline
print('left_peak:', left_peak)
print('right_peak:', right_peak)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary, binary, binary))*255
#left_peak = 0
#right_peak = 0
leftx = np.empty(shape=(0,0))
lefty = np.empty(shape=(0,0))
left_fit = np.empty(shape=(0,0))
left_win_rects = np.empty(shape=(0,0))
rightx = np.empty(shape=(0,0))
righty = np.empty(shape=(0,0))
right_fit = np.empty(shape=(0,0))
right_win_rects = np.empty(shape=(0,0))
if left_peak > 0:
(leftx, lefty), left_fit, left_win_rects = detect_line(binary, left_peak, verbose=True)
if right_peak > 0:
(rightx, righty), right_fit, right_win_rects = detect_line(binary, right_peak, verbose=True)
print('left_fit: ', left_fit)
print('right_fit: ', right_fit)
out_img = draw_lanes_with_windows(binary,
leftx, lefty, left_fit,
rightx, righty, right_fit,
left_win_rects, right_win_rects)
plt.figure(figsize=(8,8))
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.imshow(out_img)
Out[10]:
In [11]:
from detect_lane import detect_line_in_roi, draw_detect_line_in_roi
leftx1=[]
lefty1=[]
left_fit1=[]
if len(left_fit):
(leftx1, lefty1), left_fit1 = detect_line_in_roi(binary, left_fit)
(rightx1, righty1), right_fit1 = detect_line_in_roi(binary, right_fit)
out_img = draw_detect_line_in_roi(binary, left_fit1, leftx1, lefty1, right_fit1, rightx1, righty1)
plt.figure(figsize=(8,8))
plt.imshow(out_img)
plt.xlim(0, out_img.shape[1])
plt.ylim(out_img.shape[0], 0)
Out[11]:
In [12]:
from detect_lane import Line
from detect_lane import process_image_ex
def process_image(img):
return process_image_ex(img, leftL, rightL, frame_Ind, verbose=Verbose)
In [13]:
Verbose=True
frame_Ind = 0;
# Read in a thresholded image
img = mpimg.imread('test_images/test5.jpg')
leftL = Line(327, img.shape[1], 7)
rightL = Line(1018, img.shape[1], 7)
In [14]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
result = process_image(img)
result = process_image(img)
plt.figure()
plt.figure(figsize=(10,8))
plt.imshow(result)
plt.show()
plt.imsave(out_dir + 'projected_lane_test5.jpg',result)
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